A Model to Calculate Amazon EC2 Instance Performance in Frost Prediction Applications

Last week the First HPCLATAM – CLCAR Joint Conference took place in Valparaiso, Chile. There, a joint work with Prof. Carlos García Garino‘s research group (Universidad Nacional de Cuyo, Argentina) was presented. This work, entitled “A Model to Calculate Amazon EC2 Instance Performance in Frost Prediction Applications” has been published by Springer through its Communications in Computer and Information Science series.

CARLA2014

Frosts are one of the main causes of economic losses in the Province of Mendoza, Argentina. Although it is a phenomenon that happens every year, frosts can be predicted using Agricultural Monitoring Systems (AMS). AMS provide information to start and stop frosts defense systems and thus reduce economic losses. In recent years, the emergence of infrastructures called Sensor Clouds improved AMS in several aspects such as scalability, reliability, fault tolerance, etc. Sensor Clouds use Wireless Sensor Networks (WSN) to collect data in the field and Cloud Computing to store and process these data. Currently, Cloud providers like Amazon offer different instances to store and process data in a profitable way. Moreover, due to the variety of offered instances arises the need for tools to determine which is the most appropriate instance type, in terms of execution time and economic costs, for running agro-meteorological applications. In this paper we present a model targeted to estimate the execution time and economic cost of Amazon EC2 instances for frosts prediction applications.

 

J.L. Vázquez-Poletti

 

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